A wet-clutch, stepped ratio transmission may incorporate the function of being able to learn control settings and system parameters during operation of a vehicle the wet-clutch, stepped ratio transmission is incorporated in. Monitoring the variation of the control settings and the system parameters over short and long term periods of time provides many benefits. Monitoring the variation allows a system to diagnose transmission problems, to predict a remaining useful life of the transmission or a transmission component, or predict a time of failure of the transmission or a transmission component. Furthermore, such information regarding remaining useful life, component wear, and component failure can be used to adapt how the transmission is controlled.
In most vehicle controllers, especially in the automotive sector, information gathered from a plurality of sensors (both from control systems and purpose-specific sensors) may be used in a diagnostics and prognostics system to detect component failure and to assist in a maintenance schedule of a vehicle including the diagnostics and prognostics system.
Typically, on-board diagnostic (OBD) systems used in vehicles use one-way communication, which is most commonly used for signaling problems to an operator of the vehicle. The on-board diagnostic system, in some cases, may be configured to communicate over a wireless connection to other devices.
Prognostics systems are used less often in vehicles, especially in off-highway applications, where maintenance is typically planned after a certain number of operating hours, other pre-determined intervals, or after a predetermined number of clutch engagements, for example.
Recently, wet clutch transmissions have been developed that include systems incorporating learning parameters. When performing a shift between two gears, for example, a pressure profile may be analyzed to determine if the shift is correctly performed. If one or more anomalies are detected, the parameters may be adapted using online algorithms. Non-limiting examples of parameters which may be adjusted using such algorithms are fill pressure, timing (a duration of a filling of the clutch), and kiss pressure.
Learned parameters might vary because of real working conditions being different from a set of initial operating conditions for which the transmission was designed. Non-limiting examples of such conditions are temperature fluctuation and lubricant variability. Such parameters will also vary over time because of a wearing of the clutches, an oxidation of the lubricant, or other effects. Such variations over time can be interpreted to help the diagnostic and prognostic system by providing the system with additional information.
It would be advantageous to develop a vehicle controller including a diagnostics and prognostics module used with a vehicle transmission that provides enhanced interaction within and to and from the controller.